56 citations · 197 across the 22 of their papers we have counts for
5 papers · 1 filter
Marginal Release Under Local Differential Privacy
Tejas Kulkarni, Graham Cormode, Divesh Srivastava
Many analysis and machine learning tasks require the availability of marginal statistics on multidimensional datasets while providing strong privacy guarantees for the data subject…
Learning Graphical Models from a Distributed Stream
Yu Zhang, Srikanta Tirthapura, Graham Cormode
A current challenge for data management systems is to support the construction and maintenance of machine learning models over data that is large, multi-dimensional, and evolving.…
Fast Sketch-based Recovery of Correlation Outliers
Graham Cormode, Jacques Dark
Many data sources can be interpreted as time-series, and a key problem is to identify which pairs out of a large collection of signals are highly correlated. We expect that there w…
Constrained Differential Privacy for Count Data
Graham Cormode, Tejas Kulkarni, Divesh Srivastava
Concern about how to aggregate sensitive user data without compromising individual privacy is a major barrier to greater availability of data. The model of differential privacy has…
Independent Set Size Approximation in Graph Streams
Graham Cormode, Jacques Dark, Christian Konrad
We study the problem of estimating the size of independent sets in a graph defined by a stream of edges. Our approach relies on the Caro-Wei bound, which expresses the desired…